from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from ..image_utils import load_image


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__a    :	Dict         =    logging.get_logger(__name__)


__a    :	List[str]         =    Dict[str, Any]
__a    :	Tuple         =    List[Prediction]




@add_end_docstrings(_UpperCAmelCase					)
class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__(*lowerCAmelCase__ ,  **lowerCAmelCase__		)

							if self.framework == "tf":
											raise ValueError(F"The {self.__class__} is only available in PyTorch."		)

							requires_backends(self ,  '''vision'''		)
							self.check_model_type(
							    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  **lowerCAmelCase__		)	->   str:

							'''simple docstring'''


							__lowercase					     =				{}
							if "threshold" in kwargs:
											__lowercase					     =				kwargs['''threshold''']
							return {}, {}, postprocess_kwargs





			def __call__(  self ,  *lowerCAmelCase__ ,  **lowerCAmelCase__		)	->   Union[Predictions, List[Prediction]]:

							'''simple docstring'''


							return super().__call__(*lowerCAmelCase__ ,  **lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				load_image(lowerCAmelCase__		)
							__lowercase					     =				torch.IntTensor([[image.height, image.width]]		)
							__lowercase					     =				self.image_processor(images=[image] ,  return_tensors='''pt'''		)
							if self.tokenizer is not None:
											__lowercase					     =				self.tokenizer(text=inputs['''words'''] ,  boxes=inputs['''boxes'''] ,  return_tensors='''pt'''		)
							__lowercase					     =				target_size
							return inputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				model_inputs.pop('''target_size'''		)
							__lowercase					     =				self.model(**lowerCAmelCase__		)
							__lowercase					     =				outputs.__class__({'''target_size''': target_size, **outputs}		)
							if self.tokenizer is not None:
											__lowercase					     =				model_inputs['''bbox''']
							return model_outputs





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=0.9		)	->   Tuple:

							'''simple docstring'''


							__lowercase					     =				model_outputs['''target_size''']
							if self.tokenizer is not None:
											# This is a LayoutLMForTokenClassification variant.
											# The OCR got the boxes and the model classified the words.
											__lowercase     ,	__lowercase					     =				target_size[0].tolist()

											def unnormalize(lowerCAmelCase__		):
															return self._get_bounding_box(
															    torch.Tensor(
															        [
															            (width * bbox[0] / 10_00),
															            (height * bbox[1] / 10_00),
															            (width * bbox[2] / 10_00),
															            (height * bbox[3] / 10_00),
															        ]		)		)

											__lowercase     ,	__lowercase					     =				model_outputs['''logits'''].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
											__lowercase					     =				[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
											__lowercase					     =				[unnormalize(lowerCAmelCase__		) for bbox in model_outputs['''bbox'''].squeeze(0		)]
											__lowercase					     =				['''score''', '''label''', '''box''']
											__lowercase					     =				[dict(zip(lowerCAmelCase__ ,  lowerCAmelCase__		)		) for vals in zip(scores.tolist() ,  lowerCAmelCase__ ,  lowerCAmelCase__		) if vals[0] > threshold]
							else:
											# This is a regular ForObjectDetectionModel
											__lowercase					     =				self.image_processor.post_process_object_detection(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)
											__lowercase					     =				raw_annotations[0]
											__lowercase					     =				raw_annotation['''scores''']
											__lowercase					     =				raw_annotation['''labels''']
											__lowercase					     =				raw_annotation['''boxes''']

											__lowercase					     =				scores.tolist()
											__lowercase					     =				[self.model.config.idalabel[label.item()] for label in labels]
											__lowercase					     =				[self._get_bounding_box(lowerCAmelCase__		) for box in boxes]

											# {"scores": [...], ...} --> [{"score":x, ...}, ...]
											__lowercase					     =				['''score''', '''label''', '''box''']
											__lowercase					     =				[
											    dict(zip(lowerCAmelCase__ ,  lowerCAmelCase__		)		)
											    for vals in zip(raw_annotation['''scores'''] ,  raw_annotation['''labels'''] ,  raw_annotation['''boxes''']		)
											]

							return annotation





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__		)	->   Dict[str, int]:

							'''simple docstring'''


							if self.framework != "pt":
											raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''		)
							__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				box.int().tolist()
							__lowercase					     =				{
							    '''xmin''': xmin,
							    '''ymin''': ymin,
							    '''xmax''': xmax,
							    '''ymax''': ymax,
							}
							return bbox
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					UNetaDModel
			__a    :					ScoreSdeVeScheduler





			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__		)	->   Union[str, Any]:

							'''simple docstring'''


							super().__init__()
							self.register_modules(unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 20_00 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  **lowerCAmelCase__ ,  )	->   Union[ImagePipelineOutput, Tuple]:

							'''simple docstring'''


							__lowercase					     =				self.unet.config.sample_size
							__lowercase					     =				(batch_size, 3, img_size, img_size)

							__lowercase					     =				self.unet

							__lowercase					     =				randn_tensor(lowerCAmelCase__ ,  generator=lowerCAmelCase__		) * self.scheduler.init_noise_sigma
							__lowercase					     =				sample.to(self.device		)

							self.scheduler.set_timesteps(lowerCAmelCase__		)
							self.scheduler.set_sigmas(lowerCAmelCase__		)

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											__lowercase					     =				self.scheduler.sigmas[i] * torch.ones(shape[0] ,  device=self.device		)

											# correction step
											for _ in range(self.scheduler.config.correct_steps		):
															__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
															__lowercase					     =				self.scheduler.step_correct(lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		).prev_sample

											# prediction step
											__lowercase					     =				model(lowerCAmelCase__ ,  lowerCAmelCase__		).sample
											__lowercase					     =				self.scheduler.step_pred(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  generator=lowerCAmelCase__		)

											__lowercase     ,	__lowercase					     =				output.prev_sample, output.prev_sample_mean

							__lowercase					     =				sample_mean.clamp(0 ,  1		)
							__lowercase					     =				sample.cpu().permute(0 ,  2 ,  3 ,  1		).numpy()
							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return (sample,)

							return ImagePipelineOutput(images=lowerCAmelCase__		)